Study Proves Gradient Descent Can Train Any Neural Network Architecture

A research paper published on arXiv explores the universality of gradient descent as a training method for neural networks. The study investigates whether gradient descent can theoretically optimize any neural network configuration, regardless of its architecture. The findings contribute to the theoretical foundations of deep learning by addressing longstanding questions about the general applicability of this widely used optimization technique. The paper provides mathematical grounding for understanding why gradient descent works broadly across diverse neural network models.